Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pantheon-org/tekhne --skill plain-englishgit clone --depth 1 https://github.com/pantheon-org/tekhneWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pantheon-org/tekhne/plain-english)<a href="https://agentmods.dev/skills/pantheon-org/tekhne/plain-english"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/plain-english/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pantheon-org/tekhne/plain-english"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/plain-english.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00128 | $0.01334 |
| Opus 5 | $0.00064 | $0.00667 |
| Sonnet 5 | $0.00026 | $0.00267 |
| Haiku 4.5 | $0.00013 | $0.00133 |
Grade A, and why
plain-english scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plain English Writing
Translates technical content into decision-ready communication for non-technical stakeholders.
When to Use
Apply when writing for: executives, business managers, compliance/legal, or cross-functional stakeholders without the requesting team's domain knowledge.
When Not to Use
Skip when writing for engineers or technical specialists. You may optionally apply selective translation for mixed audiences; consider keeping technical depth for peer reviews or developer-facing docs.
Mindset
Technical writing optimises for completeness. Plain-English writing optimises for decisions.
Before writing a single word, ask:
"What does this person need to decide or do — and what is the minimum information required for that?"
The single most reliable fix: move the recommendation to sentence one. Everything else is secondary.
Decision Framework
┌─────────────────────────────────────────────┐
│ OPENING (required) │
│ Problem + Business impact + Action needed │
├─────────────────────────────────────────────┤
│ SUPPORTING CONTEXT (only if needed) │
│ Background / Options / Tradeoffs / Timeline │
├─────────────────────────────────────────────┤
│ APPENDIX (optional) │
│ Technical detail / Metrics / Implementation │
└─────────────────────────────────────────────┘
If a reader only reads the opening, they must still know what to do.
Workflow
Step 1: Identify Audience
executive (zero jargon) | manager (minimal jargon) | compliance/legal (tech translated) | cross-functional (inline definitions). See references/constraints-and-fallbacks.md for the audience depth guide.
If unknown: Audience: unknown. Applying manager-level clarity (fallback).
Step 2: Define the Outcome
One sentence — what must the reader decide or do? Write it before drafting.
Step 3: Draft Key Message First
What ships with it
31 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .audits/2026-02-21/analysis.md 8.0 KB
- .audits/2026-02-21/audit.json 138 B
- .audits/2026-02-21/remediation-plan.md 222 B
- .audits/2026-02-22/analysis.md 8.0 KB
- .audits/2026-02-22/audit.json 138 B
- .audits/2026-02-22/remediation-plan.md 222 B
- .audits/2026-02-23/analysis.md 8.1 KB
- .audits/2026-02-23/audit.json 139 B
- .audits/2026-02-23/remediation-plan.md 218 B
- .audits/2026-03-02/analysis.md 1.0 KB
- .audits/2026-03-02/audit.json 412 B
- .audits/2026-03-02/remediation-plan.md 2.5 KB
- .audits/2026-03-11/analysis.md 1.1 KB
- .audits/2026-03-11/audit.json 451 B
- .audits/2026-03-11/remediation-plan.md 1.7 KB
- .audits/latest 10 B
- .tessl-plugin/plugin.json 370 B
- CHANGELOG.md 1.1 KB
- evals/scenario-01.md 2.8 KB
- evals/scenario-02.md 2.7 KB
- evals/scenario-03.md 2.9 KB
- evals/scenario-04.md 2.4 KB
- evals/scenario-05.md 2.8 KB
- evals/scenario-06.md 2.8 KB
- evals/scenario-07.md 3.2 KB
- evals/scenario-08.md 3.5 KB
- references/anti-patterns.md 4.1 KB
- references/audience-types.md 1.1 KB
- references/before-after-examples.md 5.0 KB
- references/constraints-and-fallbacks.md 3.7 KB
- references/jargon-translations.md 653 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 148 lines · 128 tokens per session scan A ab7200cffb95
plain-english is a skill published in the GitHub repository pantheon-org/tekhne (10 stars, last pushed yesterday), licensed MIT. It adds 128 tokens to every session and 1,334 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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